Skip to main content

Memcached bulk api (spymemcached v/s memcached-client)

We use memcached as a cache to front mysql and we do caching at granular level. As we are a cloud filesystem company,  we cache files by path and folders by path. The problems comes when someone renames a top level folder that has 100K or more files in the hierarchy. The db operation is fast because in db all we need to do is one folder update (files are stored in normalized fashion so no rename is required at file level in db) but rename in memcache means add/delete of 100K keys. So that's like 200K operations and production code was spending close to 2 mins in memcache only. We use memcached-client java library in our code.  Memcache protocol doesn't support bulk-api so there was no easy solution than trying to set it in different threads but one colleague came across this interesting optimization that spymemcached guys has done spymemcached write optimizations.

I thought of writing a test program to compare memcached-client v/s spymemcached and spymemcached rocks.

1. On my local laptop(connected to a local memcache)  100K add is 2 sec and 100K delete is 800 msec using spymemcached whereas it took 8 sec for 100K add and 6 sec for 100K deletes using memcached-client.
1. Connecting to a remote memcache and doing 100K adds took 6 sec using spymemcached and 40 sec using memcached-client.

Here are my Test programs
=============SpyMemcached================
import java.net.InetSocketAddress;
import net.spy.memcached.MemcachedClient;
import java.io.*;
public class TestSpyMemcached {
    public static void main(String[] args) throws IOException {
        String msg="{\"name\":\"test\",\"cName\":\"test\",\"cPath\":\"/shared/marketing/engineering/test\",\"path\":\"/Shared/marketing/Engineering/test\",\"folderId\":\"c08a0d93-8216-4262-b8ec-f1f24c9f9844\",\"parentId\":\"7d9e6fbc-9d0d-4bf8-90e3-aaa924df0777\",\"ctime\":1340051093864}";
    MemcachedClient c=new MemcachedClient(
            new InetSocketAddress(args[0], Integer.parseInt(args[1])));
        long start = System.currentTimeMillis();       
        for(int i=0;i<100000;i++) {
            c.set("key"+i, 3600, msg+i);
            if(i%1000==0) {
               System.out.println("added " + i + " objects");
            }   
        }
        System.out.println(System.currentTimeMillis()-start);
        start = System.currentTimeMillis();       
        for(int i=0;i<100000;i++) {
            c.delete("key"+i);
            if(i%1000==0) {
               System.out.println("deleted " + i + " objects");
            }   
        }
        System.out.println(System.currentTimeMillis()-start);
        c.shutdown();
    }
}

=============Memcached-client================
import java.net.InetSocketAddress;
import com.meetup.memcached.MemcachedClient;
import com.meetup.memcached.SockIOPool;
import java.io.*;
public class TestMemcachedClient {
    public static void main(String[] args) throws IOException {
        String msg="{\"name\":\"test\",\"cName\":\"test\",\"cPath\":\"/shared/marketing/engineering/test\",\"path\":\"/Shared/marketing/Engineering/test\",\"folderId\":\"c08a0d93-8216-4262-b8ec-f1f24c9f9844\",\"parentId\":\"7d9e6fbc-9d0d-4bf8-90e3-aaa924df0777\",\"ctime\":1340051093864}";

        SockIOPool pool = SockIOPool.getInstance();
        String[] memcacheServers = new String[]{args[0] + ":" + args[1]};
        pool.setServers(memcacheServers);
        pool.setHashingAlg(SockIOPool.NEW_COMPAT_HASH);
        pool.initialize();
        MemcachedClient mclient = new MemcachedClient();
            mclient.setSanitizeKeys(false);
            mclient.setCompressEnable(false);
        mclient.setPrimitiveAsString(true);
        long start = System.currentTimeMillis();       
        for(int i=0;i<100000;i++) {
            mclient.set("key"+i, msg+i, 3600);
            if(i%1000==0) {
               System.out.println("added " + i + " objects");
            }   
        }
        System.out.println(System.currentTimeMillis()-start);
        start = System.currentTimeMillis();       
        for(int i=0;i<100000;i++) {
            mclient.delete("key"+i);
            if(i%1000==0) {
               System.out.println("deleted " + i + " objects");
            }   
        }
        System.out.println(System.currentTimeMillis()-start);
    }
}

Comments

Popular posts from this blog

Haproxy and tomcat JSESSIONID

One of the biggest problems I have been trying to solve at our startup is to put our tomcat nodes in HA mode. Right now if a customer comes, he lands on to a node and remains there forever. This has two major issues: 1) We have to overprovision each node with ability to handle worse case capacity. 2) If two or three high profile customers lands on to same node then we need to move them manually. 3) We need to cut over new nodes and we already have over 100+ nodes.  Its a pain managing these nodes and I waste lot of my time in chasing node specific issues. I loath when I know I have to chase this env issue. I really hate human intervention as if it were up to me I would just automate thing and just enjoy the fruits of automation and spend quality time on major issues rather than mundane task,call me lazy but thats a good quality. So Finally now I am at a stage where I can put nodes behing HAProxy in QA env. today we were testing the HA config and first problem I immediat...

Adding Jitter to cache layer

Thundering herd is an issue common to webapp that rely on heavy caching where if lots of items expire at the same time due to a server restart or temporal event, then suddenly lots of calls will go to database at same time. This can even bring down the database in extreme cases. I wont go into much detail but the app need to do two things solve this issue. 1) Add consistent hashing to cache layer : This way when a memcache server is added/removed from the pool, entire cache is not invalidated.  We use memcahe from both python and Java layer and I still have to find a consistent caching solution that is portable across both languages. hash_ring and spymemcached both use different points for server so need to read/test more. 2) Add a jitter to cache or randomise the expiry time: We expire long term cache  records every 8 hours after that key was added and short term cache expiry is 2 hours. As our customers usually comes to work in morning and access the cloud file server it ...

Spring 3.2 quartz 2.1 Jobs added with no trigger must be durable.

I am trying to enable HA on nodes and in that process I found that in a two test node setup a job that has a frequency of 10 sec was running into deadlock. So I tried upgrading from Quartz 1.8 to 2.1 by following the migration guide but I ran into an exception that says "Jobs added with no trigger must be durable.". After looking into spring and Quartz code I figured out that now Quartz is more strict and earlier the scheduler.addJob had a replace parameter which if passed to true would skip the durable check, in latest quartz this is fixed but spring hasnt caught up to this. So what do you do, well I jsut inherited the factory and set durability to true and use that public class DurableJobDetailFactoryBean extends JobDetailFactoryBean {     public DurableJobDetailFactoryBean() {         setDurability(true);     } } and used this instead of JobDetailFactoryBean in the spring bean definition     <bean i...